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Julien Stoehr
Julien Stoehr
Université Paris-Dauphine, Université PSL, CEREMADE
Verified email at ceremade.dauphine.fr - Homepage
Title
Cited by
Cited by
Year
Componentwise approximate Bayesian computation via Gibbs-like steps
G Clarté, CP Robert, RJ Ryder, J Stoehr
Biometrika 108 (3), 591-607, 2021
382021
Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
J Stoehr, N Friel
Artificial Intelligence and Statistics, 921-929, 2015
252015
Faster Hamiltonian Monte Carlo by learning leapfrog scale
C Wu, J Stoehr, CP Robert
arXiv preprint arXiv:1810.04449, 2018
232018
A review on statistical inference methods for discrete Markov random fields
J Stoehr
arXiv preprint arXiv:1704.03331, 2017
222017
Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields
J Stoehr, P Pudlo, L Cucala
Statistics and Computing 25, 129-141, 2015
18*2015
Noisy Hamiltonian Monte Carlo for doubly intractable distributions
J Stoehr, A Benson, N Friel
Journal of Computational and Graphical Statistics 28 (1), 220-232, 2019
132019
Hidden Gibbs random fields model selection using block likelihood information criterion
J Stoehr, JM Marin, P Pudlo
Stat 5 (1), 158-172, 2016
82016
Composite likelihood inference for the Poisson log-normal model
J Stoehr, SS Robin
arXiv preprint arXiv:2402.14390, 2024
42024
GiRaF: a toolbox for Gibbs Random Fields analysis
J Stoehr, P Pudlo, N Friel
R package version 1 (1), 2020
42020
Méthodes de Monte Carlo
J STOEHR
UNIVERSITÉ PARIS DAUPHINE, Département MIDO Master 1, 2019-2020, 2020
42020
Statistical inférence methods for Gibbs random fields
J Stoehr
HAL 2015, 2015
22015
Simulating signed mixtures
CP Robert, J Stoehr
Statistics and Computing 35 (1), 1-21, 2025
2025
Importance sampling-based gradient method for dimension reduction in Poisson log-normal model
B Batardière, J Chiquet, J Kwon, J Stoehr
arXiv preprint arXiv:2410.00476, 2024
2024
A gradient approximation with importance sampling for dimension reduction in natural exponential families
B Batardière, J Chiquet, J Kwon, J Stoehr
55èmes journées de Statistiques de la SFdS, 2024
2024
Simulating signed mixtures
J Stoehr, CP Robert
arXiv preprint arXiv:2401.16828, 2024
2024
Méthodes d'inférence statistique pour champs de Gibbs
J Stoehr
Université Montpellier, 2015
2015
Poisson lognormal models for count data
J Chiquet, M Mariadassou, S Robin, B Batardière, J Kwon, J Stoehr
Component-wise Approximate Bayesian Computation via Gibbs-like steps
CPR GRegoire CLARTe, RJ RYDER, J STOEHR
A toolbox for Gibbs Random Fields analysis
J Stoehr, P Pudlo, N Friel
Criteres de choix de modele pour champs de Gibbs cachés
J Stoehr, JM Marin, P Pudlo
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Articles 1–20